EXMD 634
Course description:
This course is typically taught during the fall semester to graduate students in Experimental Medicine and other disciplines within the Faculty of Medicine at McGill University. It is an introductory statistics course with motivating examples will be drawn from both clinical research and basic science research. These methods are necessary for students to carry out their own research as well as to interpret research publications. This course serves as a foundation for more advanced courses in statistical modeling. The R statistical software environment and R Studio interface are used for computation.
Assessment:
Assessment will be based primarily on individual assignments. There will also be inclass miniquizzes and a group project.
Suggested Reference:
 Statistics for the life sciences, Myra Samuels, Jeffrey Wittmer and Andrew Schaffner, 5^{th} edition, Pearson 2016 (student ebook available)
DATE  TOPIC  ASSIGNMENT 

September 2 (Lecture 1) 
• Introduction to the course • Sample size, precision, bias • Random sampling and randomization • Reporting guidelines 

• Introduction to R  
September 9 (Lecture 2) 
• Types of variables • Types of observational units • Types of study design • Laws of probability 

• Normal distribution • Binomial distribution • Random sampling and randomization • Poisson or negative binomial distribution 

September 16 (Lecture 3) 
• Central limit theorem  Assignment 1 due 
• Confidence intervals for a single mean  
September 23 (Lecture 4) 
• Confidence intervals for comparison of means • Sample size calculation based on confidence intervals 

• Hypothesis testing for a single mean and for comparison of means • Hypothesis testing vs Confidence intervals 

September 30 (Lecture 5) 
• Example problems involving ttests for one or two sample means  Assignment 2 due 
• Sample size calculation based on hypothesis tests (Type I vs. Type II errors) 

October 7 (Lecture 6) 
• Bayesian inference for one or two means  
• Probability of a wrong decision with hypothesis testing  
October 14 (Lecture 7) 
• Inference for a single proportion or comparison of two proportions: Confidence interval estimation • Sample size calculations based on confidence intervals • Inference for a single proportion or comparison of two proportions: Hypothesis testing 
Assignment 3 due 
• Sample size calculations based on hypothesis tests  
October 21 (Lecture 8) 
• Hypothesis tests for contingency tables (Chisquared test, Fisher’s exact test)  
October 28 (Lecture 9) 
• Nonparametric tests (sign test, Wilcoxon signed rank test, Wilcoxon rank sum test) Bootstrap Confidence Intervals 
Assignment 4 due 
November 4 (Lecture 10) 
• Oneway ANOVA • Null hypothesis and Ftest • Between and withingroups variance • Testing multiple comparisons 

November 11 (Lecture 11) 
• Twoway ANOVA • Randomized block design (or Repeated measures ANOVA) • Correlation 
Assignment 5 due 
November 18 (Lecture 12) 
• Simple linear regression: Model assumptions and estimation • Multiple Linear regression with two predictors 

November 25  Presentation of Course Project and submission of final course report  
December 2  Assignment 6 due 
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